Adaptive cognitive radio energy-harvesting scheme using sequential game approach

Abstract

Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to overcome the barriers that prevent the real-world wireless device deployment. For the next-generation wireless networks, they can be key techniques. In this study, we develop a novel energy-harvesting scheme for the cognitive radio (CR) network system. Using the sequential game model, data transmission and energy harvesting in each device are dynamically scheduled. Our approach can capture the wireless channel state while considering multiple device interactions. In a distributed manner, individual devices adaptively adjust their decisions based on the current system information while maximizing their payoffs. When the channel selection collision occurs, our sequential bargaining process coordinates this problem to optimize a social fairness. Finally, we have conducted extensive simulations. The results demonstrate that the proposed scheme achieves an excellent performance for the energy efficiency and spectrum utilization. The main contribution of our work lies in the fact that we shed some new light on the trade-off between individual wireless devices and CR network system.